SalesTechStar Interview with Ari Widlansky, Managing Director and COO – US for Esker

Ari Widlansky, Managing Director and COO (US) for Esker chats about the effect of automation and SaaS on order processing cycles and how AI can help bridge operational gaps:

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How has Esker as a platform evolved over the years, we’d love to hear more about the Esker Synergy AI product, Synergy Transformer, and how it enables end users.

We started offering cloud solutions 20 years ago and introduced machine learning technology in 2011 with our auto-learning technology that automatically learns how to extract data from customer orders received via fax or email, based on user corrections. The idea was to free people from data entry, a tedious task that adds little to no value to Customer Service jobs. Still with that idea in mind to minimize data entry, we then launched our first deep learning neural network in 2018 to capture data from orders seen for the first time. We actually introduced the Esker Synergy AI terminology at that time.

This year, we have released the 3^rd version of this neural network which is called Synergy Transformer and it uses the same technology as GPT. Along the way, we have also added NLP abilities to understand free-text orders where the information appears in an unstructured manner within an email body, rather than in a semi-structured document such as a PDF. Apart from extracting key data from customer orders, we have also used AI technology to automatically identify and triage orders within an email flow containing other requests, determine the priority of an order, and identify its type (typically a standard vs. a change or a return order). AI is also leveraged to verify order data and automatically detect anomalies in product quantities.

All of those AI technologies empower Customer Service Representatives in their daily job, releasing them from manual tasks such as data entry or verification so that they can focus on higher-value tasks and spend more time serving customers.

Can you talk about some of the most innovative sales tech and AI platforms from around the world that have made order processing easier and how?

I would actually argue that there have not been a lot of innovative platforms that leverage AI and aim to simplify commerce between companies. A large proportion of B2B commerce relies on e-commerce and EDI platforms, but those channels are usually pushed by one side and are not always effective for both parties. Typically, business buyers placing orders on e-commerce platforms often need to keep track of their orders through their internal systems, so it is not as easy as sending a PDF order. The same applies to EDI platforms, which require a lot of configuration before being able to automate orders and lack flexibility.

Our approach relies on AI to bridge the gap between customers and suppliers, ensuring that customers can place orders in any way they want and are not forced by their suppliers to use specific channels. A few companies, including some startups leveraging the recent advances in AI, have taken this technology approach to automatically extract data from any order and ensure both sides get value out of an automated solution.

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How can a more streamlined order processing model lead to higher customer retention rates?

A streamlined order processing model increases speed while decreasing errors. Simply put, customers getting their orders on time and without errors are going to be happy and will keep doing business with a reliable supplier.

Also, automation on the supplier side means that their Customer Service staff will have more time to serve customers, be proactive, and provide advice and recommendations, as opposed to doing busy work in the back-office.

What are some of the biggest challenges in most order processing models that you’d like to highlight here?

Building on what I said earlier about B2B e-commerce and EDI platforms, forcing customers to place orders according to your company’s preference rather than their preference is not the best approach. Customer-supplier relationships should be beneficial for both sides. That’s the first challenge I see with common order processing models. The second challenge is that while it makes sense to automate, I don’t think you can automate everything for the order processing model; it should be flexible and have room to manage exceptions. Typically, some orders require discussion and clarification, sometimes externally with the customer and sometimes also internally, to verify an unusual quantity, resolve a credit issue, propose a product replacement in case of unavailability, and more. Also associated with adaptability and flexibility, the order processing model should be able to change easily. Rather than having all rules set in stone, it should be possible to automatically adapt to changes, such as updates to a customer’s order template, or to accommodate new requirements with minimal configuration as the company evolves.

A few thoughts on the impact of AI on sales and internal finance/sales recovery processes and how you see it change the game of the sales cycle down the line?

The processing of customer orders and their entry into the ERP system is an important part of the Order-to-cash process, even though it is often overlooked with O2C (Order-to-cash) being a synonym of I2C (Invoice-to-Cash) and only considered as a financial process starting from an order already created in the ERP. However, if your order processing is not optimized, this will cause problems downstream in the I2C process. These problems typically result in unhappy customers who dispute invoices and delay payments because they did not receive their orders on time, in the right location, with incorrect quantities, or with price discrepancies. Making the order process reliable and ensuring issues are resolved upstream facilitates the collections process at the end of the cycle. This requires Customer Service, Sales, and Finance teams to collaborate and communicate smoothly and involves breaking the silos that often appear in large organizations between departments that have different objectives, tools, and managers.

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